The Surveillance Research Program (SRP) at the Division of Cancer Control and Population Sciences (DCCPS) of NCI has developed a series of statistical methods for the analysis of spatial patterns in cancer data. These include a spatial scan statistic (SaTScan, developed in collaboration with Dr. Martin Kulldorff), a spatial outlier detection tool (MGPS, developed in collaboration with Dr. William DuMouchel), a median-based spatial smoother (Headbang, developed in collaboration with Dr. Katherine Hansen), and a spatial-temporal model of cancer incidence (developed by Dr. Linda Pickle). SRP has also developed statistical methods to detect non-spatial patterns in cancer surveillance data like Joinpoint, a linear spline tool to detect changes in temporal trends. Statistical and geographical visualization tools including Micromaps (developed in collaboration with Dr. Dan Carr), ESTAT ? the Exploratory Spatial-Temporal Analysis Tool (developed in collaboration with Dr. Alan MacEachren), and CIrank (developed in collaboration with Dr. Shunpu Zhang) are among the tools that are frequently utilized in detecting cancer patterns and trends. Finally, SRP has successfully led efforts in predicting US- and state-level cancer counts using spatial and spatio-temporal projection methods for both mortality and incidence. SRP currently uses these tools in many ways for cancer surveillance and reporting activities. With the recent release of SEER cancer incidence data, the need has arisen to develop statistical and visualization approaches to identify high and low clusters of cancer incidence, as well as to provide software tools to perform such analyses.